Traceability in the agricultural sector: A review for the period 2017 – 2022
DOI:
https://doi.org/10.15517/am.v34i2.51828Keywords:
measurement, product traceability, supply chains, technology, production dataAbstract
Introduction. Traceability is regarded in business systems as a monitoring and control tool that is centered on measuring and gathering data for efficient resource allocation. The agricultural sector is no stranger to this practice because, like other industrial systems, it integrates control needs at the level of cultivation, supply of inputs, transformation, transportation, and marketing of products. Objective. To identify objects and scopes of monitoring, analysis units, and adoption of traceability trends in the agricultural supply chain, in order to reference the development of recent studies and publications that integrate this control function in this sector. Development. The applied methodology was developed through the search, selection, and analysis of articles in scientific repositories such as Science Direct and AGRIS, to identify trends in agricultural traceability in the years 2017 to 2022. Application and integration trends of traceability systems were recognized in the agricultural sector around different approaches, including digitization and information security, measurement of agricultural productivity and environmental impact mainly within the concept of sustainability. Lines of research are presented in its conclusions, as well as the knowledge gaps for future work. Conclusions. The results of the review in the last six years frame traceability trends mainly in the digital monitoring of cultivation processes, the measurement of productivity, and the environmental impact. The degree of direct intervention in the producer represents the highest proportion in the category of the logistic scope of traceability. Therefore, it is recommended in the future the development of traceability systems that monitor productivity, environmental, and social impact indicators in a convergent manner, as well as the integrated participations of actors in the agricultural sector, including producers, technical advisors, and government entities.
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References
Ampatzidis, Y., Partel, V., & Costa, L. (2020). Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence. Computers and Electronics in Agriculture, 174, Article 105457. https://doi.org/10.1016/j.compag.2020.105457
Anand, S., & Barua, M. K. (2022). Modeling the key factors leading to post-harvest loss and waste of fruits and vegetables in the agri-fresh produce supply chain. Computers and Electronics in Agriculture, 198, Article 106936. https://doi.org/10.1016/j.compag.2022.106936
Aoudji, A. K. N., Avocevou-Ayisso, C., Adégbidi, A., Gbénou, C., & Lebailly, P. (2017). Upgrading opportunities in agricultural value chains: Lessons from the analysis of the consumption of processed pineapple products in southern Benin. African Journal of Science, Technology, Innovation and Development, 9(6), 729–737. https://doi.org/10.1080/20421338.2016.1163472
Boente, C., Matanzas, N., Garcia-González, N., Rodriguez-Valdés, E., & Gallego, J. R. (2017). Trace elements of concern affecting urban agriculture in industrialized areas: A multivariate approach. Chemosphere, 183, 546–556. https://doi.org/10.1016/j.chemosphere.2017.05.129
Bosona, T., Gebresenbet, G., & Olsson, S. -O. (2018). Traceability system for improved utilization of solid biofuel from agricultural prunings. Sustainability, 10(2), Article 258. https://doi.org/10.3390/su10020258
Castillo Landínez, S. P., Caicedo Rodríguez, P. E., & Sánchez Gómez, D. F. (2019). Diseño e implementación de un software para la trazabilidad del proceso de beneficio del café. Ciencia & Tecnología Agropecuaria, 20(3), 523–536. https://doi.org/10.21930/rcta.vol20_num3_art:1588
Chen, Y., Li, Y., & Li, C. (2020). Electronic agriculture, blockchain and digital agricultural democratization: Origin, theory and application. Journal of Cleaner Production, 268, Article 122071. https://doi.org/10.1016/j.jclepro.2020.122071
Cruz, S., Boza, A., & Alemany, M. (2018). Traceability in the Food Supply Chain: Review of the literature from a technological perspective. Dirección y Organización, 64, 50–55. http://hdl.handle.net/10251/121089
Dananjayan, S., Tang, Y., Zhuang, J., Hou, C., & Luo, S. (2022). Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images. Computers and Electronics in Agriculture, 193, Article 106658. https://doi.org/10.1016/j.compag.2021.106658
Donner, M., Verniquet, A., Broeze, J., Kayser, K., & De Vries, H. (2021). Critical success and risk factors for circular business models valorising agricultural waste and by-products. Resources, Conservation and Recycling, 165, Article 105236. https://doi.org/10.1016/j.resconrec.2020.105236
Durrant, A., Markovic, M., Matthews, D., May, D., Enright, J., & Leontidis, G. (2022). The role of cross-silo federated learning in facilitating data sharing in the agri-food sector. Computers and Electronics in Agriculture, 193, Article 106648. https://doi.org/10.1016/j.compag.2021.106648
Ehlers, M. -H., Huber, R., & Finger, R. (2021). Agricultural policy in the era of digitalisation. Food Policy, 100, Article 102019. https://doi.org/10.1016/j.foodpol.2020.102019
Food and Agricultural Organization, & World Health Organization. (2013). Codex Alimentarius Commission: Procedual Manual (21th ed.). Retrieved February 18th, 2022 from https://www.fao.org/3/i3243e/i3243e.pdf
Gao, F., Fang, W., Sun, X., Wu, Z., Zhao, G., Li, G., Li, R., Fu, L., & Zhang, Q. (2022). A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard. Computers and Electronics in Agriculture, 197, Article 107000. https://doi.org/10.1016/j.compag.2022.107000
Gao, D., Qiao, L., An, L., Sun, H., Li, M., Zhao, R., Tang, W., & Song, D. (2022). Diagnosis of maize chlorophyll content based on hybrid preprocessing and wavelengths optimization. Computers and Electronics in Agriculture, 197, Article 106934. https://doi.org/10.1016/j.compag.2022.106934
Gebresenbet, G., Bosona, T., Olsson, S. -O., & Garcia, D. (2018). Smart system for the optimization of logistics performance of the pruning biomass value chain. Applied Sciences, 8(7), Article 1162. https://doi.org/10.3390/app8071162
Gongal, A., Karkee, M., & Amatya, S. (2018). Apple fruit size estimation using a 3D machine vision system. Information Processing in Agriculture, 5(4), 498–503. https://doi.org/10.1016/j.inpa.2018.06.002
Hossein Ronaghi, M. (2020). A blockchain maturity model in agricultural supply chain. Information Processing in Agriculture, 8(3), 398–408. https://doi.org/10.1016/j.inpa.2020.10.004
Hu, S., Huang, S., Huang, J., & Su, J. (2021). Blockchain and edge computing technology enabling organic agricultural supply chain: A framework solution to trust crisis. Computers and Industrial Engineering, 153, Article 107079. https://doi.org/10.1016/j.cie.2020.107079
International Organization for Standarization. (2007). ISO/TC 176/SC 1 22005:2007, Traceability in the feed and food chain: General principles and basic requirements for system design and implementation. Retrieved February 18th, 2022 from https://www.iso.org/standard/36297.html
International Organization for Standarization. (2015). ISO 9000:2015, Quality management systems — Fundamentals and vocabulary. Retrieved February 18th, 2022 from https://www.iso.org/standard/45481.html
Jes Petersen, R., Blicher-Mathiesen, G., Rolighed, J., Estrup Andersen, H., & Kronvang, B. (2021). Three decades of regulation of agricultural nitrogen losses: Experiences from the Danish Agricultural Monitoring Program. Science of The Total Environment, 787, Article 147619. https://doi.org/10.1016/j.scitotenv.2021.147619
Jin, C. -Y., Levi, R., Liang, Q., Renegar, N., & Zhou, J. -H. (2021). Food safety inspection and the adoption of traceability in aquatic wholesale markets: A game-theoretic model and empirical evidence. Journal of Integrative Agriculture, 20(10), 2807–2819. https://doi.org/10.1016/S2095-3119(21)63624-9
Kumar Dubey, P., Singh, A., Chaurasia, R., Kumar Pandey, K., Bundela, A., Kant Dubey, R., & Chirakkuzhyil Abhilash, P. (2021). Planet friendly agriculture: Farming for people and the planet. Current Research in Environmental Sustainability, 3, Article 100041. https://doi.org/10.1016/j.crsust.2021.100041
Law, E. A., Macchi, L., Baumann, M., Decarre, J., Gavier-Pizarro, G., Levers, C., Mastrangelo, M. E., Murray, F., Müller, D., Piquer-Rodríguez, M., Torres, R., Wilson, K. A., & Kuemmerle, T. (2021). Fading opportunities for mitigating agriculture-environment trade-offs in a south American deforestation hotspot. Impacto ambiental, 262, Article 109310. https://doi.org/10.1016/j.biocon.2021.109310
Leng, K., Bi, Y., Jing, L., Fu, H. -C., & Van Nieuwenhuyse, I. (2018). Research on agricultural supply chain system with double chain architecture based on blockchain technology. Future Generation Computer Systems, 86, 641–649. https://doi.org/10.1016/j.future.2018.04.061
Li, L., Paudel, K. P., & Guo, J. (2021). Understanding Chinese farmers’ participation behavior regarding vegetable traceability systems. Food Control, 130, Article 108325. https://doi.org/10.1016/j.foodcont.2021.108325
Liu, L., Zuo, Z. -T., Xu, F. -R., & Wang, Y. -Z. (2020). Study on quality response to environmental factors and geographical traceability of wild Gentiana rigescens Franch. Frontiers in Plant Science, 11, Article 1128. https://doi.org/10.3389/fpls.2020.01128
Lu, W., Luo, H., He, L., Duan, W., Tao, Y., Wang, X., & Li, S. (2022). Detection of heavy metals in vegetable soil based on THz spectroscopy. Computers and Electronics in Agriculture, 197, Article 106923. https://doi.org/10.1016/j.compag.2022.106923
Lu, Y., & Young, S. (2020). A survey of public datasets for computer vision tasks in precision agriculture. Computers and Electronics in Agriculture, 178, Article 105760. https://doi.org/10.1016/j.compag.2020.105760
Maas, B., Fabian, Y., Kross, S. M., & Ritchter, A. (2021). Divergent farmer and scientist perceptions of agricultural biodiversity, ecosystem services and decision-making. Biological Conservation, 256, Article 109065. https://doi.org/10.1016/j.biocon.2021.109065
Maguire, T. J., Wellen, C., Stammler, K. L., & Mundle, S. O. C. (2018). Increased nutrient concentrations in Lake Erie tributaries influenced by greenhouse agriculture. Science of the Total Environment, 633, 433–440. https://doi.org/10.1016/j.scitotenv.2018.03.188
Mancipe-Castro, L., & Gutierrez-Carvajal, R. E. (2021). Prediction of environment variables in precision agriculture using a sparse model as data fusion strategy. Information Processing in Agriculture, 9(2), 171–183. https://doi.org/10.1016/j.inpa.2021.06.007
Masudin, I., Ramadhani, A., & Palupi Restuputri, D. (2021). Traceability system model of Indonesian food cold-chain industry: A Covid-19 pandemic perspective. Cleaner Engineering and Technology, 4, Article 100238. https://doi.org/10.1016/j.clet.2021.100238
Min Aung, M., & Seok Chang, Y. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172–184. https://doi.org/10.1016/j.foodcont.2013.11.007
Mirbod, O., Choi, D., Thomas, R., & He, L. (2021). Overcurrent-driven LEDs for consistent image colour and brightness in agricultural machine vision applications. Computers and Electronics in Agriculture, 187, Article 106266. https://doi.org/10.1016/j.compag.2021.106266
Moberg, E., Karlsson Potter, H., Wood, A., Hansson, P. -A., & Röös, E. (2020). Benchmarking the Swedish diet relative to global and national environmental targets-Identification of indicator limitations and data gaps. Sustainability, 12(4), Article 1407. https://doi.org/10.3390/su12041407
Oberoi, H., & Dinesh, M. R. (2019). Trends and innovations in value chain management of tropical fruits. Journal of Horticultural Sciences, 14(2), 87–97. https://doi.org/10.24154/jhs.2019.v14i02.002
O’Grady, M., Langton, D., Salinari, F., Daly, P., & O’Hare, G. (2021). Service design for climate-smart agriculture. Information Processing in Agriculture, 8(2), 328–340. https://doi.org/10.1016/j.inpa.2020.07.003
Pal, A., Kumar Dubeya, S., & Goelb, S. (2022). IoT enabled microfluidic colorimetric detection platform for continuous monitoring of nitrite and phosphate in soil. Computers and Electronics in Agriculture, 195, Article 106856. https://doi.org/10.1016/j.compag.2022.106856
Park, A., & Li, H. (2021). The effect of blockchain technology on supply chain sustainability performances. Sustainability, 13(4), Article 1726. https://doi.org/10.3390/su13041726
Partel, V., Charan Kakarla, S., & Ampatzidis, Y. (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture, 157, 339–350. https://doi.org/10.1016/j.compag.2018.12.048
Patel, H., & Shrimali, B. (2021). AgriOnBlock: Secured data harvesting for agriculture sector using blockchain technology. ICT Express. https://doi.org/10.1016/j.icte.2021.07.003
Paul, K., Chatterjee, S. S., Pai, P., Varshney, A., Juikar, S., Prasad, V., Bhadra, B., & Dasgupta, S. (2022). Viable smart sensors and their application in data driven agriculture. Computers and Electronics in Agriculture, 198, Article 107096. https://doi.org/10.1016/j.compag.2022.107096
Pauschinger, D., & Klauser, F. R. (2021). The introduction of digital technologies into agriculture: Space, materiality and the public–private interacting forms of authority and expertise. Journal of Rural Studies, 91, 217–227. https://doi.org/10.1016/j.jrurstud.2021.06.015
Pivoto, D., Dabdab Waquil, P., Talamini, E., Spanhol Finocchio, C. P., Dalla Corte, V. F., & de Vargas Moraes, G. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, 5(1), 21–32. https://doi.org/10.1016/j.inpa.2017.12.002
Prashar, D., Jha, N., Jha, S., Lee, Y., & Prasad Joshi, G. (2020). Blockchain based traceability and visibility for agricultural products: A descentralized way of ensuring food safety in India. Sustainability, 12(8), Article 3497. http://doi.org/10.3390/su12083497
Pylianidis, C., Osinga, S., & Athanasiadis, I. N. (2021). Introducing digital twins to agriculture. Computers and Electronics in Agriculture, 184, Article 105942. https://doi.org/10.1016/j.compag.2020.105942
Qian, J., Wu, W., Yu, Q., Ruiz-García, L., Xiang, Y., Jiang, L., Shi, Y., Duan, Y., & Yang, P. (2020). Filling the trust gap of food safety in food trade between the EU and China. Food and Energy Security, 9(4), Article e249. https://doi.org/10.1002/fes3.249
Rettore de Araujo Zonella, R. A., da Silva, E., & Pessoa Albini, L. (2020). Security challenges to smart agriculture: Current state, key issues, and future directions. Array, 8, Article 100048. https://doi.org/10.1016/j.array.2020.100048
Riefolo, C., Belmonte, A., Quarto, R., Quarto, F., Ruggieri, S., & Castrignanò, A. (2022). Potential of GPR data fusion with hyperspectral data for precision agriculture of the future. Computers and Electronics in Agriculture, 199, Article 107109. https://doi.org/10.1016/j.compag.2022.107109
Rinsberg, H. (2014). Perspectives on food traceability: a systematic literature review. Supply Chain Management, 19(5/6), 558–576. https://doi.org/10.1108/SCM-01-2014-0026
Roux, N., Kastner, T., Erb, K. -H., & Haberl, H. (2021). Does agricultural trade reduce pressure on land ecosystems? Decomposing drivers of the embodied human appropriation of net primary production. Ecological Economics, 181, Article 106915. https://doi.org/10.1016/j.ecolecon.2020.106915
Schnebelin, E., Labarthe, P., & Touzard, J. -M. (2021). How digitalisation interacts with ecologisation? Perspectives from actors of the French Agricultural Innovation System. Journal of Rural Studies, 86, 599–610. https://doi.org/10.1016/j.jrurstud.2021.07.023
Sengupta, T., Narayanamurthy, G., Moser, R., & Kumar Hota, P. (2019). Sharing app for farm mechanization: Gold Farm’s digitized access based solution for financially constrained farmers. Computers in Industry, 109, 195–203. https://doi.org/10.1016/j.compind.2019.04.017
Shahverdi, K., Alamiyan-Harandi, F., & Maestre, J. M. (2022). Double Q-PI architecture for smart model-free control of canals. Computers and Electronics in Agriculture, 197, Article 106940. https://doi.org/10.1016/j.compag.2022.106940
Singh Thakur, P., Tiwari, B., Kumar, A., Gedam, B., Bhatia, V., Krejcar, O., Dobrovolny, M., Nebhen, J., & Prakash, S. (2022). Deep transfer learning based photonics sensor for assessment of seed-quality. Computers and Electronics in Agriculture, 196, Article 106891. https://doi.org/10.1016/j.compag.2022.106891
Smalling, K. L., Devereux, O. H., Gordon, S. E., Phillips, P. J., Blazer, V. S., Hladick, M. L., Kolpin, D. W., Meyer, M. T., Sperry, A. J., & Wagner, T. (2021). Environmental and anthropogenic drivers of contaminants in agricultural watersheds with implications for land management. Science of the Total Environment, 774, Article 145687. https://doi.org/10.1016/j.scitotenv.2021.145687
Tohidyan Far, S., & Rezaei-Moghaddam, K. (2018). Impacts of the precision agricultural technologies in Iran: An analysis experts’ perception & their determinants. Information Processing in Agriculture, 5(1), 173–184. https://doi.org/10.1016/j.inpa.2017.09.001
Traore, O., Wei, C., & Rehman, A. (2021). Investigating the performance of agricultural sector on well-being: New evidence from Burkina Faso. Journal of the Saudi Society of Agricultural Sciences, 21(4), 232–241. https://doi.org/10.1016/j.jssas.2021.08.006
Villanueva-de la Cruz, N., Soto-Estrada, A., Arvizu-Barrón, E., Asiain-Hoyos, A., & Leos-Rodríguez, J. A. (2020). System of traceability in the Supply Chain of Malanga in Veracruz. Tropical and Subtropical Agroecosystems, 23(3), Article 81. https://www.revista.ccba.uady.mx/ojs/index.php/TSA/article/view/2937/1470
Wang, L., Fang, S., Pei, Z., Wu, D., Zhu, Y., & Zhuo, W. (2022). Developing machine learning models with multisource inputs for improved land surface soil moisture in China. Computers and Electronics in Agriculture, 192, Article 106623. https://doi.org/10.1016/j.compag.2021.106623
Wang, T., Hardin IV, R. G., Ward, J. K., Wanjura, J. D., & Barnes, E. M. (2022). A smart cotton module tracking and monitoring system for handling logistics and cover damage. Computers and Electronics in Agriculture, 193, Article 106620. https://doi.org/10.1016/j.compag.2021.106620
Wang, M. -C., & Yang, C. -Y. (2019). Analysing the traceability system in herbal product industry by game theory. Agricultural Economics, 65(2), 74–81. https://doi.org/10.17221/102/2018-AGRICECON
World Bank. (2018). The role of digital identification in agriculture. Open Knowledge Repository of the Worl Bank. http://hdl.handle.net/10986/31216
World Bank. (2020). Indonesia agro-value chain assessment: Issues and options in promoting digital agriculture. Open Knowledge Repository of the Worl Bank. https://openknowledge.worldbank.org/handle/10986/34069
Xu, S. -W., Wang, Y., Wang, S. -W., & Li, J. -Z. (2020). Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information. Journal of Integrative Agriculture, 19(10), 2582–2596. https://doi.org/10.1016/S2095-3119(20)63368-8
Xu, Y., Zhang, B., & Zhang, L. (2018). A technical efficiency evaluation system for vegetable production in China. Information Processing in Agriculture, 5(3), 345–353. https://doi.org/10.1016/j.inpa.2018.05.001
Yang, F., Wang, K., Han, Y., & Qiao, Z. (2018). A cloud-based digital farm management system for vegetable production process management and quality traceability. Sustainability, 10(11), Article 4007. https://doi.org/10.3390/su10114007
Zhao, T., & Nakano, A. (2018). Agricultural product authenticity and geographical origin traceability: Use of nondestructive measurement. Japan Agricultural Research Quarterly, 52(2), 115–122. https://doi.org/10.6090/jarq.52.115
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